Best Practices in Utilizing the Changing Criterion Design Behavior Analysis in Practice

Table Of Content

• D-5: Use single-subject experimental designs (e.g., Reversal, Multiple Baseline, Multielement, Changing Criterion) ©
• Single case design: A critique of methodology and analysis of recent trends
• Criteria to Be Successful in Fashion Design
• #3. Barbie Fashion Design Maker App
• Some methodological considerations in the design and implementation of behavior therapy research
• Table 6
• Fun Fashion Designer Games Perfect for Creative Kids

Over the past 70 years, single-case design (SCD) research has evolved to include a broad array of methodological and analytic advances. In this article, we describe some of these advances and discuss how SCDs can be used to optimize behavioral health interventions. Specifically, we discuss how parametric analysis, component analysis, and systematic replications can be used to optimize interventions.

D-5: Use single-subject experimental designs (e.g., Reversal, Multiple Baseline, Multielement, Changing Criterion) ©

The immediate decrease in the level of smoking for participant 1 when the treatment is introduced also implicates the treatment. We can also detect, however, an increasing trend in the early portion of the treatment condition. Thus, we need to continue the treatment condition until there is no undesirable trend before returning to the baseline condition. But, the meaningfulness of this effect requires additional considerations (see the section below on “Visual, Statistical, and Social Validity Analysis”).

Single case design: A critique of methodology and analysis of recent trends

It may be that the client has such a unique history and profile of symptoms, the clinician may not be confident about the predictive validity of a particular intervention for his or her client [6]. Furthermore, the use of SCDs in practice conforms to the scientist-practitioner ideal espoused by training models in clinical psychology and allied disciplines [78]. Several conclusions can be drawn about the effects of the various components in changing behavior.

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Only a small amount of studies used both reinforcement and punishment without another intervention (10.1 %) and fewer studies used punishment alone (.7 %). Of the 196 interventions with contingent reinforcement, 167 (85.2 %) utilized fixed-ratio schedules (FR) and 29 used variable ratio (VR). All 61 interventions with contingent punishment implemented punishment on fixed-ratio schedules, specifically FR-1 in 58 interventions (95.1 %). The features of the CCD suggest its function as a research tool or an intervention tool; however, due to the systematic nature of the CCD, these descriptors become mutually exclusive.

As the visual analyst progresses through the steps, he or she also uses the six features to evaluate effects within and across experimental phases. A component analysis is “any experiment designed to identify the active elements of a treatment condition, the relative contributions of different variables in a treatment package, and/or the necessary and sufficient components of an intervention” [69]. Behavioral health interventions often entail more than one potentially active treatment element.

Some methodological considerations in the design and implementation of behavior therapy research

To clarify, in a study conducted by Schleien et al. (1981), two adults with severe handicaps were part of a procedure to hit the dartboard with darts. These individuals were required to throw three darts and hit the board each time in order to progress to a further distance or height closer to the standard 5 ft, 8 in. Thus, the behavioral performance of throwing three darts with 100 % accuracy was stable and the task topography of distance and height changed. There are two potential limitations of reversal designs in the context of behavioral health interventions. However, we think that the benefits of demonstrating that a treatment works outweigh the risks of temporarily withdrawing treatment (in most cases).

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Pre- and Post-Intervention and Intervention Characteristics

The CCD is a flexible, basic design versatile in terms of applied population, target behavior, and setting, while also capable of demonstrating adequate experimental control. However, since its primary presentation in 1976, the CCD has been infrequently used as a research/intervention tool. The purpose of this paper is to examine the CCD research that has been conducted and characteristics of its use and implementation to date. The changing criterion design (CCD) utilizes step-wise benchmarks for manipulating a dimension (i.e., accuracy, frequency, duration, latency, or magnitude) of a single behavior already present in an individual’s repertoire (Cooper et al. 2007).

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Just as engineering methods seek to isolate and combine active treatment components to optimize interventions, so too do single-case methods. The main difference between approaches is the focus on the individual as the unit of analysis in SCDs. In a multiple-baseline design, the durations of the baselines vary systematically for each participant in a so-called staggered fashion. For example, one participant may start treatment after five baseline days, another after seven baseline days, then nine, and so on.

Studies containing a CCD were identified through a search of PsycINFO, ERIC, and PubMed databases using the key word changing criterion as well as articles referencing Hartmann and Hall (1976). The introduction and discussion section of each article was examined to locate other studies utilizing a CCD. Articles containing both original research data and a statement that a CCD was used were included in this study. All articles identified that were published prior to January 2014 were included. A pilot run of this study was conducted with ten articles to better shape data collection and variables to be included. Parametric analysis involves exposing participants to a range of values of the independent variable, as opposed to just one or two values.

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This discussion leads to a type of generality called scientific generality [63], which is at the heart of a scientific understanding of behavioral health interventions (or any intervention for that matter). As described by Branch and Pennypacker [63], scientific generality is characterized by knowledgeable reproducibility, or knowledge of the factors that are required for a phenomenon to occur. Scientific generality can be attained through parametric and component analysis, and through systematic replication. One advantage of a single-case approach to establishing generality is that a series of strategic studies can be conducted with some degree of efficiency. Moreover, the data intimacy afforded by SCDs can help achieve scientific generality about behavioral health interventions.

Visual analysis of the time-series data revealed that medication effects were apparent within about 1–2 weeks of exposure, making a 4-week trial unnecessary. This discovery was replicated in a number of subjects and led them to optimize future, larger studies by only conducting a 2-week intervention. Investigating different treatment durations, such as this, is also a parametric analysis. Many behavioral health interventions can be delivered at different levels [66] and are therefore amenable to parametric analysis.

But breaking down a larger goal into tiny habits does help build momentum for change. And the Tiny Habits Method helps remind you that to initiate a change, make it easy and fun. Another form of optimization is an understanding of the conditions under which an intervention may be successful. These conditions may relate to particular characteristics of the participant (or whatever the unit of analysis happens to be) or to different situations.